Claude Sonnet 5 (Non-reasoning, High Effort) vs o3: The Ultimate Performance & Pricing Comparison

Claude Sonnet 5 is the safer default for developers who need documented availability and stronger measured general intelligence, while o3 is cheaper and substantially faster.

The comparison is unusually asymmetric. Artificial Analysis reports a general intelligence score for both models, but coding data appears only for Claude Sonnet 5 and math data appears only for o3. The official sources also describe very different product states. Anthropic lists Claude Sonnet 5 as an available model with a documented API alias and several cloud channels, while OpenAI’s current model directory does not list o3. That difference matters for production planning as much as benchmark performance.

The practical choice is therefore not simply a contest between two scores. Claude Sonnet 5 offers the stronger documented platform case and an Artificial Analysis Intelligence Index of 41.7. o3 offers an Intelligence Index of 30.4, a median output speed of 128.056 tokens per second, and a lower blended price. Developers should choose based on whether measured breadth and deployment confidence outweigh throughput and unit economics.

Claude Sonnet 5 leads the available general intelligence comparison, but o3 leads speed and blended price.

Artificial Analysis gives Claude Sonnet 5 an Intelligence Index of 41.7 and o3 an Intelligence Index of 30.4. The data supports a clear result on that index, but it does not establish a complete capability ranking. Claude Sonnet 5 has a reported Coding Index of 66.4, while o3 has a reported Math Index of 88.3. Because the corresponding score is missing for each other model, the evidence does not support declaring a coding winner or a math winner between them.

The cost picture is narrower than the blended figure suggests. Input pricing is tied at $2 per 1M tokens. o3 costs $8 per 1M output tokens, compared with $10 for Claude Sonnet 5, producing blended prices of $3.5 and $4. For workloads dominated by generated text, o3’s lower output rate can matter. For workloads dominated by prompts, that advantage disappears.

Anthropic documents text and image input, multilingual capability, vision, a stable API alias, and access through several providers. OpenAI’s supplied documentation does not confirm equivalent current details for o3. Anthropic documents Claude Sonnet 5’s capabilities and access channels, while OpenAI’s current model directory does not list o3.

o3 is the faster model in the supplied measurements, but Claude Sonnet 5 has the stronger measured general intelligence result and more complete capability documentation.

The output-speed gap is large enough to change product behavior. o3 reaches a median of 128.056 output tokens per second, while Claude Sonnet 5 reaches 64.222. That difference can improve the perceived responsiveness of streaming interfaces, coding assistants, and interactive debugging sessions. It can also increase the amount of work a system completes during a fixed operating window.

Latency does not separate the models in the supplied data. Both report 0.3 seconds. This means faster generation should not automatically be interpreted as faster time to first response. For a user-facing application, prompt processing, tool calls, output length, and orchestration may still determine the total wait. The available figures show a generation advantage for o3, not a universal responsiveness advantage.

Claude Sonnet 5 has the stronger general intelligence score, with 41.7 versus 30.4 for o3. Its reported Coding Index of 66.4 is useful evidence for developer workloads, but it is not a direct head-to-head coding comparison because o3’s coding value is absent. o3’s Math Index of 88.3 is similarly important for mathematical tasks, yet Claude Sonnet 5’s corresponding value is absent. A team selecting for coding or mathematics should run its own task set before treating either specialized result as decisive.

Anthropic also states that Claude Sonnet 5 supports Adaptive thinking but not Extended thinking through the enabled thinking parameter. The official page does not provide effort-level latency or cost figures, so the effect of its default high effort remains unquantified. Anthropic documents these thinking controls.

o3 is cheaper on blended and output pricing, but Claude Sonnet 5 can remain competitive when prompts dominate or when its capabilities reduce downstream work.

The headline difference is modest at the blended level: o3 costs $3.5 per 1M blended tokens, compared with $4 for Claude Sonnet 5. The more consequential difference appears on output tokens. Claude Sonnet 5 costs $10 per 1M output tokens, while o3 costs $8. Input pricing is equal at $2 per 1M tokens.

That structure changes which model is cheaper in practice. A retrieval-heavy application that sends large prompts and generates short answers will see little benefit from o3’s output advantage. A code-generation workflow that produces long patches, explanations, or test files will be more sensitive to output pricing. The chart can show the rates, but it cannot show how prompt length, answer length, retries, tool calls, and validation affect the final bill.

Claude Sonnet 5 also introduces a tokenization risk for teams forecasting from characters or words. Anthropic says its newer tokenizer usually produces about 30% more tokens for the same text, although the increase depends on content and workload. That can make an apparently equal input price translate into a higher effective cost than a character-based estimate suggests. Anthropic explains the pricing and tokenizer behavior.

The official pricing evidence is incomplete for o3. OpenAI’s supplied pricing page does not list a current Standard, Batch, Flex, or Fast mode price for o3. Artificial Analysis supplies comparison prices, but developers should verify their actual account-level route before committing to a production budget. OpenAI’s pricing documentation is the relevant source for current o3 listing status.

Developers should choose Claude Sonnet 5 as the default production candidate unless o3’s speed and output economics directly match the workload.

Pick Claude Sonnet 5 when the application needs a documented current model, broad provider access, image input, vision, multilingual behavior, or stronger evidence on general intelligence. Anthropic lists access through the Claude API, Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry. That gives platform teams more deployment paths and may reduce integration risk when infrastructure requirements change.

Pick o3 when fast generation is central to the product experience, output volume is high, and the team has already verified that o3 remains callable through its intended OpenAI route. Its 128.056 median output tokens per second and $3.5 blended price create a strong case for high-throughput interactive systems. The case is weaker if the product depends on a currently documented stable alias, detailed context limits, or confirmed multimodal behavior, because the supplied OpenAI source does not establish those facts.

Do not use the reported specialized scores as a substitute for a task-based evaluation. Claude Sonnet 5’s Coding Index of 66.4 cannot be compared directly with an absent o3 coding score. o3’s Math Index of 88.3 cannot be compared directly with an absent Claude Sonnet 5 math score. The evidence is sufficient to identify tradeoffs, not to prove a universal winner for every developer workload.

A sensible evaluation should measure accepted patches, test correctness, tool-call completion, time to usable answer, and cost per successful task. The supplied research contains no reliable community posts with verified methods for coding feel, speed perception, or model quirks, so anecdotal reputation should not fill the evidence gap.

Claude Sonnet 5 is the better starting point when a team values documented production access and broader measured intelligence evidence.

The main uncertainty is o3’s current product status. The supplied OpenAI model directory does not list it, and the supplied official materials do not confirm its current API availability, stable alias, context window, output limit, or multimodal support. That does not prove that o3 cannot be used. It means the team must verify access before building around it.

Claude Sonnet 5 also has operational constraints. Its synchronous Messages API has a maximum output of 128k tokens, and its documented thinking support distinguishes Adaptive thinking from Extended thinking. High effort is the default in Claude API and Claude Code, but Anthropic does not provide effort-specific latency or cost values in the verified source.

The strongest conclusion is conditional. Claude Sonnet 5 is the more defensible general-purpose choice from the available evidence. o3 is the more attractive throughput choice when fast generation and lower output cost outweigh documentation uncertainty. Data provided by https://artificialanalysis.ai/